Altering autonomous or semi-autonomous vehicle operation based on route traversal values

ABSTRACT

A method is disclosed for mitigating the risks associated with operating an autonomous or semi-autonomous vehicle by using calculated route traversal values to select less risky travel routes and/or modify vehicle operation. Various approaches to achieving this risk mitigation are presented. A computing device is configured to generate a database of route traversal values. This device may receive a variety of historical route traversal information, real-time vehicle information, and/or route information from one of more data sources and calculate a route traversal value for the associated driving route. Subsequently, the computing device may provide the associated route traversal value to other devices, such as a vehicle navigation device associated with the autonomous or semi-autonomous vehicle. An insurance company may use this information to help determine insurance premiums for autonomous or semi-autonomous vehicles by analyzing and/or mitigating the risk associated with operating those vehicles.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. application Ser. No.14/849,045 filed Sep. 9, 2015, entitled “ALTERING AUTONOMOUS ORSEMI-AUTONOMOUS VEHICLE OPERATION BASED ON ROUTE TRAVERSAL VALUES.” Thecontents of the above noted application are hereby incorporated byreference in their entirety.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. This summary is not anextensive overview of the disclosure. It is intended neither to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

In accordance with some aspects of the disclosure, a computing system isand methods are disclosed for dynamically routing a vehicle, which maybe an autonomous vehicle or a semi-autonomous vehicle. The system mayreceive or otherwise have access to various types of information,including but not limited to, accident information, geographicinformation, vehicle information, and route traversal information, whichmay be stored in a data store (e.g., database) and/or received from oneor more data sources. Using such information, the system may select oneor more routes for routing the vehicle. In making the selection, thesystem may determine additional information (such as one or more routetraversal values) for one or more associated road segments of one ormore routes and provide the determined information for the roadsegment(s). In one embodiment, separate information can be determinedfor vehicles engaged in autonomous or semi-autonomous driving over theroad segment and/or vehicles engaged in manual driving over the roadsegment. The system may then use the determined information to determinea route for the autonomous or semi-autonomous vehicle. The determinedinformation may also be used to adjust driving characteristics of theautonomous or semi-autonomous vehicle. For example, a vehicle may travelmore slowly along a route due to a higher route traversal value. Thus,by assigning certain information (such as route traversal values) to oneor more road segments and using that assigned information to selectcertain routes, the vehicle may follow a route that is considered more(or even most) appropriate for that particular vehicle under certainconditions.

According to further aspects, a first device or system such as apersonal navigation device, mobile device, personal computing device,and/or vehicle autonomous (or semi-autonomous) driving system for afirst vehicle may be in communication with a second device or systemsuch as another personal navigation device, mobile device, personalcomputing device, and/or vehicle autonomous (or semi-autonomous) drivingsystem for a second vehicle. Information collected by the system for thesecond vehicle may be communicated to the first vehicle, which mayincorporate this information in to the route traversal value for itstravel route. The information communicated between the vehicles may beused to analyze available travel routes and select a route which is moreappropriate for the vehicle under the known conditions (e.g., a routethat presents less risk of accident.

Example details of these and other aspects of the disclosure are setforth in the accompanying drawings and description below. Other featuresand advantages of aspects of the disclosure will be apparent from thedescription and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the disclosure may take physical form in certain parts andsteps, embodiments of which will be described in detail in the followingdescription and illustrated in the accompanying drawings that form apart hereof, wherein:

FIG. 1 is a block diagram depicting an illustrative operatingenvironment in accordance with aspects of the disclosure.

FIG. 2 is a flow chart depicting illustrative steps for calculating aroute traversal value of a route in accordance with aspects of thedisclosure.

FIG. 3 is a flow chart depicting illustrative steps for determining andproviding route traversal values to a computing device in accordancewith aspects of the disclosure.

FIG. 4 is a flow chart depicting illustrative steps for calculating theroute traversal value of a travel route in accordance with aspects ofthe disclosure.

FIG. 5 is a flow chart depicting illustrative steps for calculating andrecalculating route traversal values, in accordance with aspects of thedisclosure.

FIG. 6 is a flow chart depicting illustrative steps for analyzinghistorical accident information in accordance with aspects of thedisclosure.

FIG. 7 is a flow chart depicting illustrative steps for analyzinghistorical accident information to adjust driving actions of anautonomous vehicle over a travel route in accordance with aspects of thedisclosure.

FIG. 8 is a flow chart depicting illustrative steps for analyzinghistorical accident information to select a travel route in accordancewith aspects of the disclosure.

FIG. 9 is a block diagram depicting an illustrative operatingenvironment in communication with a second vehicle, in accordance withaspects of the disclosure.

It will be apparent to one skilled in the art after review of theentirety disclosed that the steps illustrated in the figures listedabove may be performed in other than the recited order, and that one ormore steps illustrated in these figures may be optional.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

In some embodiments, a personal navigation device, mobile device,personal computing device, and/or vehicle autonomous driving system maycommunicate with a data store (e.g., database) of informationrepresenting route traversal values. The device(s) may receiveinformation about a travel route and use that information to retrieveroute traversal values for road segments in the travel route. Theaggregate of the route traversal values may be sent for display on ascreen of the device and/or for recording in memory of the device. Thecontents of memory or another data storage device may also be uploadedto a data store for use by, e.g., insurance companies, to determinewhether to adjust a quote for insurance coverage or one or more aspectsof current insurance coverage such as premium, specific coverages,specific exclusions, rewards, special terms, etc.

Route traversal values may be or otherwise include, for instance, one ormore values associated with the risks of operating a vehicle, such as avehicle 100 as shown in FIG. 9, via one or more routes (e.g., roads,waterways, pathways, greenways, trails, flight routes, etc.). The routetraversal values may indicate or otherwise depend upon one or moreestimated or otherwise determined risks to, e.g., the vehicle, risks toa driver of the vehicle, risks to the driver's property, risks toothers, and/or risks to others' property. For example, a busy interstatemay have one or more high route traversal values for a car (or othertype of vehicle) operated at high speeds. These high route traversalvalues may represent a high risk of serious injury to a driver of thecar, a high chance of damage to the car, and/or a high risk of causingdamage to another driver and/or car. As another example, a boat (orother vehicle) may have a low route traversal value when operated atspeeds up to 20 MPH, but may have rapidly increasing route traversalvalues when operated at speeds past 40 MPH due to the difficulties ofhandling the boat. Route traversal values may also take into accountother factors. For example, a boat may have a higher route traversalvalue when operating on Memorial Day weekend and/or when dragging aninner tube due to the increased chance of collision or personal injury.While cars and boats have been described, a vehicle may be any type ofvehicle, including but not limited to a car (which can include a truckof any size, a van, etc.), a boat, a motorcycle, an airplane, ahelicopter, a bicycle, a moped, and the like.

In some embodiments, in accordance with aspects of the disclosure, apersonal navigation device, mobile device, personal computing device,and/or vehicle autonomous driving system may access the database ofroute traversal values to assist in identifying and presenting alternatelow-risk travel routes. The driver, operator, or autonomous drivingsystem may select among the various travel routes presented, taking intoaccount risk tolerance and/or cost of insurance. Depending on the routeselection, the vehicle's insurance policy may be adjusted accordingly,for either the current insurance policy or a future insurance policy.

In certain embodiments, vehicle sensors, vehicle OBD, vehiclecommunication systems, and/or other devices or systems disclosed hereinmay collect, transmit, and/or receive data pertaining to autonomous orsemi-autonomous driving of the vehicles. In autonomous orsemi-autonomous driving, the vehicle fulfills all or part of the drivingwithout being piloted by a human. An autonomous or semi-autonomous carcan be also referred to as a driverless car, self-driving car, or robotcar. For example, in autonomous or semi-autonomous driving, a vehiclecontrol computer may be configured to operate all or some aspects of thevehicle driving, including but not limited to acceleration,deceleration, steering, and/or route navigation. A vehicle with anautonomous or semi-autonomous driving capability may sense itssurroundings using the vehicle sensors and/or receive inputs regardingcontrol of the vehicle from the vehicle communications systems,including but not limited to short range communication systems,telematics, or other vehicle communication systems.

Referring to FIG. 1, an example of a suitable operating environment inwhich various aspects of the disclosure may be implemented is shown inthe architectural diagram of FIG. 1. The operating environment is onlyone example of a suitable operating environment and is not intended tosuggest any limitation as to the scope of use or functionality of thedisclosures. The operating environment may be comprised of one or moredata sources 104, 106 in communication with a computing device 102. Thecomputing device 102 may use information communicated from the datasources 104, 106 to generate values that may be stored in a conventionaldatabase format. In one embodiment, the computing device 102 may be ahigh-end server computer with one or more processors 114 and memory 116for storing and maintaining the values generated. The memory 116 storingand maintaining the values generated need not be physically located inthe computing device 102. Rather, the memory (e.g., ROM, flash memory,hard drive memory, RAID memory, etc.) may be located in a remote datastore (e.g., memory storage area) physically located outside thecomputing device 102, but in communication with the computing device102.

A personal computing device 108 (e.g., a personal computer, tablet PC,handheld computing device, personal digital assistant, mobile device,etc.) may communicate with the computing device 102. Similarly, apersonal navigation device 110 (e.g., a global positioning system (GPS),geographic information system (GIS), satellite navigation system, mobiledevice, vehicle autonomous or semi-autonomous driving system, otherlocation tracking device, etc.) may communicate with the computingdevice 102. The communication between the computing device 102 and theother devices 108, 110 may be through wired or wireless communicationnetworks and/or direct links. One or more networks may be in the form ofa local area network (LAN) that has one or more of the well-known LANtopologies and may use a variety of different protocols, such asEthernet. One or more of the networks may be in the form of a wide areanetwork (WAN), such as the Internet. The computing device 102 and otherdevices (e.g., devices 108, 110) may be connected to one or more of thenetworks via twisted pair wires, coaxial cable, fiber optics, radiowaves or other media. The term “network” as used herein and depicted inthe drawings should be broadly interpreted to include not only systemsin which devices and/or data sources are coupled together via one ormore communication paths, but also stand-alone devices that may becoupled, from time to time, to such systems that have storagecapability.

In another embodiment in accordance with aspects of the disclosure, apersonal navigation device 110 may operate in a stand-alone manner bylocally storing some of the database of values stored in the memory 116of the computing device 102. For example, a personal navigation device110 (e.g., a GPS in an automobile or autonomous or semi-autonomousdriving system) may comprise a processor, memory, and/or input devices118/output devices 120 (e.g., keypad, display screen, speaker, etc.).The memory may be comprised of a non-volatile memory that stores adatabase of values used in calculating an estimated route risk foridentified routes. Therefore, the personal navigation device 110 neednot communicate with a computing device 102 located at, for example, aremote location in order to calculate identified routes. Rather, thepersonal navigation device 110 may behave in a stand-alone manner anduse its processor to calculate route traversal values of identifiedroutes. If desired, the personal navigation device 110 may be updatedwith an updated database of values after a period of time (e.g., anannual patch with new route traversal values determined over the prioryear).

In yet another embodiment in accordance with aspects of the disclosure,a personal computing device 108 may operate in a stand-alone manner bylocally storing some of the database of values stored in the memory 116of the computing device 102. For example, a personal computing device108 may be comprised of a processor, memory, input device (e.g., keypad,CD-ROM drive, DVD drive, etc.), and output device (e.g., display screen,printer, speaker, etc.). The memory may be comprised of CD-ROM mediathat stores values used in calculating an estimated route risk for anidentified route. Therefore, the personal computing device 108 may usethe input device to read the contents of the CD-ROM media in order tocalculate a value for the identified route. Rather, the personalcomputing device 108 may behave in a stand-alone manner and use itsprocessor to calculate a route traversal value. If desired, the personalcomputing device 108 may be provided with an updated database of values(e.g., in the form of updated CD-ROM media) after a period of time. Oneskilled in the art will appreciate that personal computing device 108,110, 112 need not be personal to a single user; rather, they may beshared among members of a family, company, etc.

The data sources 104, 106 may provide information to the computingdevice 102. In one embodiment in accordance with aspects of thedisclosure, a data source may be a computer which contains memorystoring data and is configured to provide information to the computingdevice 102. Some examples of providers of data sources in accordancewith aspects of the disclosure include, but are not limited to,insurance companies, third-party insurance data providers, autonomous orsemi-autonomous vehicle operation providers, government entities, statehighway patrol departments, local law enforcement agencies, statedepartments of transportation, federal transportation agencies, trafficinformation services, road hazard information sources, constructioninformation sources, weather information services, geographicinformation services, vehicle manufacturers, vehicle safetyorganizations, and environmental information services. For privacyprotection reasons, in some embodiments of the disclosure, access to theinformation in the data sources 104, 106 may be restricted to onlyauthorized computing devices 102 and for only permissible purposes. Forexample, access to the data sources 104, 106 may be restricted to onlythose persons/entities that have signed an agreement (e.g., anelectronic agreement) acknowledging their responsibilities with regardto the use and security to be accorded this information.

The computing device 102 uses the information from the data sources 104,106 to generate values that may be used to calculate an estimated routerisk. Some examples of the information that the data sources 104, 106may provide to the computing device 102 include, but are not limited to,accident information, geographic information, route information, andother types of information useful in generating a database of values forcalculating an estimated route risk.

Some examples of accident information include, but are not limited to,loss type, applicable insurance coverage(s) (e.g., bodily injury,property damage, medical/personal injury protection, collision,comprehensive, rental reimbursement, towing), loss cost, number ofdistinct accidents for the segment, time relevancy validation, cause ofloss (e.g., turned left into oncoming traffic, ran through red light,rear-ended while attempting to stop, rear-ended while changing lanes,sideswiped during normal driving, sideswiped while changing lanes,accident caused by tire failure (e.g., blow-out), accident caused byother malfunction of car, rolled over, caught on fire or exploded,immersed into a body of water or liquid, unknown, etc.), impact type(e.g., collision with another automobile, collision with cyclist,collision with pedestrian, collision with animal, collision with parkedcar, etc.), drugs or alcohol involved, pedestrian involved, wildlifeinvolved, type of wildlife involved, speed of vehicle at time ofincident, direction the vehicle is traveling immediately before theincident occurred, date of incident, time of day, night/day indicator(i.e., whether it was night or day at the time of the incident),temperature at time of incident, weather conditions at time of incident(e.g., sunny, downpour rain, light rain, snow, fog, ice, sleet, hail,wind, hurricane, etc.), road conditions at time of incident (e.g., wetpavement, dry pavement, etc.), and location (e.g., geographiccoordinates, closest address, zip code, etc.) of vehicle at time ofincident, whether the vehicle was engaged in autonomous orsemi-autonomous or manual driving when the accident occurred.

In an embodiment, accident information may be categorized. For example,in an embodiment, accident information categories may include anaccident type, cause of accident, and/or probable cause of accident. Forexample, a cause of accident may include loss of vehicle control and/orcollision with wildlife. For example, a cause of accident or probablecause of accident may include excess speed and lack vehicle traction onthe road.

Accident information associated with vehicle accidents may be stored ina database format and may be compiled per road or route segment. Oneskilled in the art will understand that the term segment may beinterchangeably used to describe a road or route segment, including butnot limited to an intersection, round about, bridge, tunnel, ramp,parking lot, railroad crossing, or other feature that a vehicle mayencounter along a route.

Time relevancy validation relates to the relevancy of historicalaccident information associated with a particular location. Timerelevancy validation information may be dynamically created by comparingthe time frames of accident information to the current date. Forexample, if a location or route had many collisions prior to five yearsago but few since, perhaps a road improvement reduced the risk (such asadding a traffic light). Time relevancy information may be generatedremotely and transmitted by a data source 104, 106 to the computingdevice 102 like other information. Alternatively, time relevancyinformation may be calculated at the computing device 102 using otherinformation transmitted by a data source 104, 106. For example, theappropriateness of historical information may be related to the timeframe into which the information belongs. Examples of time frames mayinclude, but are not limited to, less than 1 year ago, 1 year ago, 2years ago, 3 years ago, 4 years ago, 5 to 10 years ago, and greater than10 years ago. In one embodiment, the more recent the historicalinformation, the greater weight is attributed to the information.

Some examples of geographic information include, but are not limited to,location information and attribute information. Examples of attributeinformation include, but are not limited to, information aboutcharacteristics of a corresponding location described by some locationinformation: posted speed limit, construction area indicator (i.e.,whether location has construction), topography type (e.g., flat, rollinghills, steep hills, etc.), road type (e.g., residential, interstate,4-lane separated highway, city street, country road, parking lot, etc.),road feature (e.g., intersection, gentle curve, blind curve, bridge,tunnel), number of intersections, whether a roundabout is present,number of railroad crossings, whether a passing zone is present, whethera merge is present, number of lanes, width of road/lanes, populationdensity, condition of road (e.g., new, worn, severely damaged withsink-holes, severely damaged with erosion, gravel, dirt, paved, etc.),wildlife area, state, county, and/or municipality. Geographicinformation may also include other attribute information about roadsegments, intersections, bridges, tunnels, railroad crossings, and otherroadway features.

Location information for an intersection may include the latitude andlongitude (e.g., geographic coordinates) of the geometric center of theintersection. The location may be described in other embodiments using aclosest address to the actual desired location or intersection. Theintersection (i.e., location information) may also include informationthat describes the geographic boundaries, for example, of theintersection which includes all information that is associated within acircular area defined by the coordinates of the center of theintersection and points within a specified radius of the center. Inanother example of location information, a road segment may be definedby the latitude and longitude of its endpoints and/or an area defined bythe road shape and a predetermined offset that forms a polygon. Segmentsmay comprise intersections, bridges, tunnels, rail road crossings orother roadway types and features. Those skilled in the art willrecognize that segments can be defined in many ways without departingfrom the spirit of this disclosed disclosure.

Some examples of vehicle information include, but are not limited to,information that describes vehicles that are associated with incidents(e.g., vehicle accidents, etc.) at a particular location (e.g., alocation corresponding to location information describing a segment,intersection, etc.) Vehicle information may include vehicle make,vehicle model, vehicle year, and age. Vehicle information may alsoinclude information collected through one or more in-vehicle devices orsystems such as an event data recorder (EDR), onboard diagnostic system,global positioning satellite (GPS) device, vehicle autonomous orsemi-autonomous driving system; examples of this information includespeed at impact, brakes applied, throttle position, direction at impact,whether the vehicle is engaged in manual or autonomous orsemi-autonomous driving. As is clear from the preceding examples,vehicle information may also include information about the driver of avehicle being driven at the time of an incident. Other examples ofdriver information may include age, gender, marital status, occupation,alcohol level in blood, credit score, distance from home, cell phoneusage (i.e., whether the driver was using a cell phone at the time ofthe incident), number of occupants.

In one embodiment in accordance with aspects of the disclosure, a datasource 104 may provide the computing device 102 with accidentinformation that is used to generate values (e.g., create new valuesand/or update existing values). The computing device 102 may use atleast part of the received accident information to calculate a value,associate the value with a road segment (or other location information),and store the value in a database format. One skilled in the art willappreciate, after thorough review of the entirety disclosed herein, thatthere may be other types of information that may be useful in generatinga database of values for use in, among other things, calculating anestimated route risk.

For example, in accordance with aspects of the disclosure, a data source104 may provide the computing device 102 with geographic informationthat is used to generate new roadway feature route traversal values in adatabase of route traversal values and/or update existing routetraversal values; where the roadway feature may comprise intersections,road segments, tunnels, bridges, or railroad crossings. Attributesassociated with roadways may also be used in part to generate routetraversal values. The computing device 102 may use at least part of thereceived geographic information to calculate a value, associate thevalue with a road segment (or other location information), and store thevalue in a database format. Numerous examples of geographic informationwere provided above. For example, a computing device 102 may receivegeographic information corresponding to a road segment comprisingaccident information and roadway feature information and then calculatea route traversal value. Therefore, when calculating a route traversalvalue, the system may use, in one example, the geographic informationand the accident information (if any accident information is provided).In some embodiments in accordance with aspects of the disclosure, thecomputing device may use accident information, geographic information,vehicle information, and/or other information, either alone or incombination, in calculating route traversal values in a database format.

The values generated by the computing device 102 may be associated witha road segment containing the accident location and stored in a datastore. Similar to a point of interest (POI) stored in GPS systems, apoint of risk (POR) is a road segment or point on a map that has riskinformation associated with it. Points of risk may arise becauseincidents (e.g., accidents) have occurred at these points before. Inaccordance with aspects of the disclosure, the road segment may be apredetermined length (e.g., ¼ mile) on a stretch of road. Alternatively,road segments may be points (i.e., where the predetermined length isminimal) on a road. Furthermore, in some embodiments, road segment mayinclude one or more different roads that are no farther than apredetermined radius from a road segment identifier. Such an embodimentmay be beneficial in a location, for example, where an unusually largenumber of streets intersect, and it may be impractical to designate asingle road for a road segment.

Referring to FIG. 2, in accordance with aspects of the disclosure, acomputing device 102 may receive accident information (in step 202),geographic information (in step 204), and/or vehicle information (instep 206). The computing device 102 may calculate (in step 212) theroute traversal value for a road segment (or point of risk) by applyingactuarial techniques to the information that may be received from datasources 104, 106. In one embodiment, the computing device 102 receivesand stores the accident information in a data store with thelatitude/longitude and time of the incident. The accident data isassociated with a location and combined with other accident dataassociated with the same location (in step 210). Applying actuarialand/or statistical modeling techniques involving multiple predictors,such as generalized linear models and non-linear models, a routetraversal value may be calculated (212), and the calculated routetraversal value may be recorded in memory (116) (in step 214). Themultiple predictors involved in the statistical model used to calculatea route traversal value may include accident information, geographicinformation, and vehicle information, including whether the vehicle wasoperating autonomously or manually at the time of the accident.Associating the route traversal value (in step 208) with a line segmentand/or point which best pinpoints the area of the road in which theincident(s) occurred may be accomplished by using established GISlocating technology (e.g., GPS ascertaining a geographicallydeterminable address, and assigning the data file to a segment's orintersection's formal address determined by the system). For example,two or more accidents located in an intersection or road segment mayhave slightly different addresses depending on where within theintersection or segment the accident location was determined to be.Therefore, the system may identify a location based on business rules.In another example business rules may identify an incident locationusing the address of the nearest intersection. In yet another examplethe system may identify the location of an incident on a highway usingsegments based on mileage markers or the lengths may be dynamicallydetermined by creating segment lengths based on relatively equalnormalized route traversal values. Therefore, roadways that havestretches with higher numbers of accidents may have shorter segmentsthan stretches that have fewer accidents. In another example, if theincident occurred in a parking lot, the entire parking lot may beassociated with a formal address that includes all accidents locatedwithin a determined area. One skilled in the art will appreciate afterreview of the entirety disclosed that road segment includes a segment ofroad, a point on a road, and other designations of a location (e.g., anentire parking lot).

For example, an insurance claim-handling processor may collect dataabout numerous incidents such as collision, theft, weather damage, andother events that cause any one of (or combination of) personal injury,vehicle damage, and damage to other vehicles or property. Informationabout the accident may be collected through artifacts such as firstnotice of loss (FNOL) reports and claim adjuster reports and may bestored in one or more data stores used by the insurer. Other data mayalso be collected at the point and time when the incident occurred, andthis information (e.g., weather conditions, traffic conditions, vehiclespeed, etc.) may be stored with the other accident information. Theinformation in these data stores may be distributed by data sources 104,106 in accordance with aspects of the disclosure. In addition, someinformation may also be recorded in third-party data sources that may beaccessible to one or more insurance companies. For example, trafficinformation (e.g., traffic volume) and weather information may beretrieved in real-time (or near real-time) from their respective datasources.

Referring to FIG. 3, in accordance with aspects of the disclosure, thecomputing device 102 may send (in step 312) the route traversal valuecorresponding to a road segment when it receives location information(in step 302) requesting the risk associated with a particular location.The particular location information may be in the form oflongitude/latitude coordinates, street address, intersection, closestaddress, or other form of information. Furthermore, in some embodimentsthe accuracy of the route traversal value may be improved by submittingthe direction that a vehicle travels (or may travel) through a roadsegment. The computing device 102 may receive (in step 304) the vehicledirection and use it to determine the route traversal value associatedwith the vehicle route. For example, a dangerous intersectiondemonstrates high risk to a vehicle/driver that passes through it.However, actuarial analysis (e.g., of data showing many recordedaccidents at the location) may show that it is more dangerous if thedriver is traveling northbound on the road segment and turns left.Therefore, the vehicle direction may also be considered when retrievingthe appropriate route traversal value (in step 310).

Likewise, the computing device 102 may also receive (in step 308) otherinformation to enhance the accuracy of the route traversal valueassociated with a travel route. For example, the computing device 102may receive (in step 306) the time of day when the driver is driving (orplans to drive) through a particular travel route. This information mayimprove the accuracy of the route traversal value retrieved (in step310) for the travel route. For example, a particular segment of roadthrough a wilderness area may have a higher rate of accidents involvingdeer during the night hours, but no accidents during the daylight hours.Therefore, the time of day may also be considered when retrieving theappropriate route traversal value (in step 310). In addition, thecomputing device may receive (in step 308) other information to improvethe accuracy of the route traversal value retrieved (in step 310) for atravel route. Some examples of this other information include, but arenot limited to, the vehicle's speed (e.g., a vehicle without a sportsuspension attempting to take a dangerous curve at a high speed),vehicle's speed compared to the posted speed limit, etc.

In accordance with aspects of the disclosure, a computer-readable mediumstoring computer-executable instructions for performing the stepsdepicted in FIGS. 2 and 3 and/or described in the present disclosure iscontemplated. The computer-executable instructions may be configured forexecution by a processor (e.g., processor 114 in computing device 102)and stored in a memory (e.g., memory 116 in computing device 102).Furthermore, as explained earlier, the computer-readable medium may beembodied in a non-volatile memory (e.g., in a memory in personalnavigation device 110) or portable media (e.g., CD-ROM, DVD-ROM, USBflash, etc. connected to personal computing device 108).

In accordance with aspects of the disclosure, a personal navigationdevice 110 may calculate a route traversal value for a travel route of avehicle. The personal navigation device 110 may be located, for example,in a driver's vehicle, as a component of an autonomous orsemi-autonomous driving system, or in a mobile device 112 with locationtracking capabilities. Alternatively, a personal computing device 108may be used to calculate the route traversal value for a travel route ofa vehicle.

For example, referring to FIG. 4, a personal navigation device 110 mayreceive (in step 402) travel route information. The travel routeinformation may include, but is not limited to, a start location, endlocation, road-by-road directions, and/or turn-by-turn directions. Thepersonal navigation device 110 may use the travel route information andmapping software to determine the road segment upon which the vehiclewill travel, and retrieve (in step 404) the route traversal value forthat road segment. For each subsequent road segment remaining in thetravel route (see step 406), the personal navigation device 110 mayaccess the database of route traversal values to retrieve (in step 404)the route traversal value for that road segment. As explained earlier,the database of route traversal values may be stored locally to thepersonal navigation device 110, or may be stored remotely and accessedthrough a wired/wireless link to the data store.

The route traversal values retrieved (in step 404) for the travel routemay be aggregated (in step 408) and a total route traversal value forthe travel route may be sent (in step 410). In an alternate embodiment,the computing device 102 may count the number of each type of road riskalong the travel route based on the values stored in the database. Thisnumber may then be multiplied by a risk-rating factor for the respectiverisk type. A risk type may comprise intersections, locations of pastaccidents along a route, railroad crossings, merges, roadway class(residential, local, commercial, rural, highways, limited accesshighways). Other risk types may include proximity to businesses thatsell alcohol, churches or bingo parlors.

The sum of this product overall risk types may, in this alternateembodiment, equal the total route traversal value. The total routetraversal value may be divided by the distance traveled to determine theroute risk category for the travel route. For example, a route riskcategory may be assigned based on a set of route traversal value rangesfor low, medium, and high risk routes.

After being aggregated, the total route traversal value may be sent (instep 410) to a viewable display on the personal navigation device 110.Alternatively, the total route traversal value may be sent (in step 410)to a local/remote memory where it may be recorded and/or monitored. Forexample, it may be desirable for a safe driver to have her total routetraversal value for all travel routes traveled over a time period to beuploaded to an insurance company's data store. The insurance company maythen identify the driver as a lower-risk driver (e.g., a driver thattravels on statistically lower-risk routes during lower-risk times) andprovide the driver/vehicle with a discount and/or credit (in step 412)on an existing insurance policy (or towards a future insurance policy).At least one benefit of the aforementioned is that safe drivers and/oroperators having safe autonomous or semi-autonomous driving systems arerewarded appropriately, while high-risk drivers and operators ofautonomous or semi-autonomous vehicles are treated accordingly.

In some embodiments in accordance with aspects of the disclosure, theroute traversal value sent (in step 410) may be in the form of a numberrating the risk of the travel route (e.g., a rating of 1 to 100 where 1is very low risk and 100 is very high risk). Alternatively, the routetraversal value may be in the form of a predetermined category (e.g.,low risk, medium risk, and high risk). At least one benefit ofdisplaying the route traversal value in this form is the simplicity ofthe resulting display for the driver. For example, an enhanced GPS unitmay display a route (or segment of a route) in a red color to designatea high risk route, and a route may be displayed in a green color todesignate a lower risk route. At least one benefit of a predeterminedcategory for the route traversal value is that it may be used as themeans for comparing the amount of risk associated with each travel routewhen providing alternate routes. In addition, the enhanced GPS unit mayalert the driver of a high risk road segment and offer the driver anincentive (e.g., monetary incentive, points, etc.) for avoiding thatsegment.

In accordance with aspects of the disclosure, a computer-readable mediumstoring computer-executable instructions for performing the stepsdepicted in FIG. 4 and/or described in the present disclosure iscontemplated. The computer-executable instructions may be configured forexecution by a processor (e.g., a processor in personal navigationdevice 110) and stored in a memory (e.g., flash memory in device 110).

When retrieving route traversal values, in accordance with aspects ofthe disclosure, one or more techniques, either alone or in combination,may be used for identifying and calculating the appropriate routetraversal value for road segments. For example, under an accident costseverity rating (ACSR) approach, each point of risk has a value whichmeasures how severe the average accident is for each point of risk. Thevalue may be normalized and/or scaled by adjusting the range of thevalues. For example, under an ACSR approach using a range of values from1 to 10: considering all accidents that occur in a predetermined area(e.g., road segment, state, zip code, municipality, etc.), the accidentsin the top ten percentile of expensive accidents in that territory mayget a 10 value and the lowest 10 percentile of costly accidents in thatregion may get a 1 value. The actual loss cost may be calculated bysumming the various itemized loss costs (e.g., bodily injury, propertydamage, medical/personal injury protection, collision, comprehensive,uninsured/underinsured motorist, rental reimbursement, towing, etc.).

In an alternate embodiment, the ACSR approach may attribute varyingweights to the different types of loss costs summed to calculate theactual loss cost. For example, after analyzing the information, certainportions of a loss cost (e.g., medical cost) may indicate risk moreaccurately than others. The importance of these portions may be weightedmore heavily in the final loss cost calculation. Actuarial methods maybe used to adjust loss cost data for a segment where a fluke accidentmay cause the calculated route traversal value to far exceed the routetraversal value based on all the other data.

Under the accidents per year (APYR) approach, in accordance with aspectsof the disclosure, each point of risk has a route traversal value thatmay reflect the average number of accidents a year for that individualpoint of risk. Under a modified APYR approach, the route traversal valuefor a point of risk continues to reflect the average number of accidentsa year, but attributes a lesser weight to accidents that occurred alonger time ago, similar to time relevancy validation (e.g., it givesemphasis to recent accident occurrences over older occurrences).

Under the risk severity (RSR) approach, in accordance with aspects ofthe disclosure, each point of risk has a route traversal value that mayreflect the severity of risk for that individual point of risk. Forexample, an intersection that is a frequent site of vehicle accidentrelated deaths may warrant a very high route traversal value under theRSR approach. In one embodiment, risk severity rating may be based onaccident frequency at intersections or in segments over a determinedperiod of time. In another embodiment, the rating may be based on losscosts associated to intersections and segments. Yet another embodimentmay combine accident frequency and severity to form a rating for asegment or intersection. One skilled in the art can recognize that riskseverity ratings may be based on one or a combination of factorsassociated with intersections or segments.

Under the Environmental Risk Variable (ERV) approach, in accordance withaspects of the disclosure, each point of risk has a route traversalvalue that may reflect any or all information that is not derived fromrecorded accidents and/or claims, but that may be the (direct orindirect) cause of an accident. In one embodiment, the route traversalvalue under the ERV approach may be derived from vehicle informationtransmitted by a data source 104, 106. In an alternate embodiment, theEVR approach may use compound variables based on the presence or absenceof multiple risk considerations which are known to frequently, orseverely, cause accidents. A compound variable is one that accounts forthe interactions of multiple risk considerations, whether environmentalor derived from recorded accidents and/or claims. For example, drivingthrough a wildlife crossing zone at dusk may generate a greater routetraversal value than driving through this same area at noon. Theinteraction of time of day and location may be the compound variable.Another example may consider current weather conditions, time of day,day of the year, and topography of the road. A compound variable may bethe type of infrequent situation which warrants presenting a verbalwarning to a driver (e.g., using a speaker system in a personalnavigation device 110 mounted in a vehicle) of a high risk route (e.g.,a high risk road segments).

Another possible approach may be to calculate the route traversal valueusing one or more of the approaches described above divided by thelength of the route traveled. This may provide an average routetraversal value for use in conjunction with a mileage rating plan. Inone embodiment, the system combines route risk and conventional mileagedata to calculate risk per mile rating.

In one embodiment, a device in a vehicle (e.g., personal navigationdevice 110, mobile device 112, etc.) may record and locally store theroute and/or the route and time during which a route was traveled. Thistravel route information may be uploaded via wireless/wired means (e.g.,cell phones, manually using a computer port, etc.). This travel routeinformation may be used to automatically query a data source 104, 106for route rating information and calculate a total route traversalvalue.

Some accident data may be recorded and locally stored on a device (e.g.,personal navigation device 110, mobile device 112, etc.) that providesincident location and a timestamp that can be used to synchronize otherdata located in data sources 104 and 106. The captured information maybe periodically uploaded to computing device 102 for further processingof accident data for updating the road segment database in memory 116.In some embodiments, the other data may include local weatherconditions, vehicle density on the roadway, and traffic signal status.Additional information comprising data from an in-vehicle monitoringsystem (e.g., event data recorder or onboard diagnostic system) mayrecord operational status of the vehicle at the time of the incident.Alternatively, if the vehicle did not have a location tracking device,an insurance claims reporter may enter the address and other informationinto the data source manually. If the vehicle was configured with anin-vehicle monitoring system that has IEEE 802.11 Wi-Fi capabilities (orany other wireless communication capabilities), the travel routeinformation may be periodically uploaded or uploaded in real-time (ornear real-time) via a computer and/or router. The in-vehicle monitoringsystem may be configured to automatically upload travel routeinformation (and other information) through a home wireless router to acomputer. In some advanced monitoring systems, weather and traffic data(and other useful information) may be downloaded (in real-time or nearreal-time) to the vehicle. In some embodiments, it may be desirable touse mobile devices 112 (with the requisite capabilities) to transmit theinformation, provide GPS coordinates, and stream in data from othersources.

The risk types described above may be variables in a multivariate modelof insurance losses, frequencies, severities, and/or pure premiums.Interactions of the variables may also be considered. The coefficientthe model produces for each variable (along with the coefficient for anyinteraction terms) may be the value to apply to each risk type. Thepersonal navigation device 110 may initially provide thequickest/shortest route from a start location A to an end location B,and then determine the route traversal value by determining either thesum product of the number of each risk type and the value for that risktype or the overall product of the number of each risk type and thevalue for that risk type. (Traffic and weather conditions could eitherbe included or excluded from the determination of the route traversalvalue for comparison of routes. If not included, an adjustment may bemade to the route traversal value once the route has been traveled). Thedriver may be presented with an alternate route which is less risky thanthe initial route calculated. The personal navigation device 110 maydisplay the difference in risk between the alternate routes and permitthe driver to select the preferred route. In some embodiments inaccordance with the disclosure, a driver/vehicle may be provided amonetary benefit (e.g., a credit towards a future insurance policy) forselecting a less risky route.

In one example in accordance with aspects of the disclosure, a drivermay enter a starting location and an end location into a personalnavigation device 110, including a personal navigation device of anautonomous or semi-autonomous driving system. The personal navigationdevice 110 may present the driver with an illustrative 2-mile route thattravels on a residential road near the following risks: 5 intersections,3 past accident sites, 1 railroad crossing, and 1 lane merging site.Assuming for illustrative purposes that the following route traversalvalues apply to the following risk types:

Risk Type Risk-rating Factor Intersections 55 Past Accidents 30 RailroadCrossing  5 Merge 60 Residential Road 2 per mile

Then, the route traversal value for the entire 2-mile route may becalculated, in one embodiment of the disclosure, as follows:

Risk Type Risk-rating Factor Count Product Intersections 55 5  55 * 5 =275 Past Accidents 30 3 30 * 3 = 90 Railroad Crossing  5 1 5 * 1 = 5Merge 60 1 60 * 1 = 60 Residential Road 2 per mile 2 2 * 2 = 4 Sum Total434

Assuming a route traversal value between 0 and 350 (per mile) iscategorized as a low-risk route, then the aforementioned 2-mile route'sroute traversal value of 217 (i.e., 434 divided by 2) classifies it alow-risk route.

In some embodiments, for rating purposes the route traversal value mayconsider the driving information of the driver/vehicle. For example, thepersonal navigation device 110 (or other device) may record the routetaken, as well as the time of day/month/year, weather conditions,traffic conditions, and the actual speed driven compared to the postedspeed limit. The current weather and traffic conditions may be recordedfrom a data source 104, 106. Weather conditions and traffic conditionsmay be categorized to determine the risk type to apply. The posted speedlimits may be included in the geographic information. For each segmentof road with a different posted speed limit, the actual speed driven maybe compared to the posted speed limit. The difference may be averagedover the entire distance of the route. In addition, various techniquesmay be used to handle the amount of time stopped in traffic, at trafficlights, etc. One illustrative technique may be to only count the amountof time spent driving over the speed limit and determine the averagespeed over the speed limit during that time. Another illustrative methodmay be to exclude from the total amount of time the portion where thevehicle is not moving. Then, upon completion of the trip, the routetraversal value may be calculated and stored in memory along with theother information related to the route risk score and mileage traveled.This information may later be transmitted to an insurance company's datastore, as was described above.

In another embodiment in accordance with aspects of the disclosure, realtime data may be used to dynamically assign route traversal values toeach point of risk. For example, some road segments may have a higherroute traversal value when a vehicle travels through at a time when,e.g., snowfall is heavy. In such situations, a dynamic route traversalvalue may be applied to the road segment to determine the appropriateroute traversal value to assign to the route.

In accordance with aspects of the disclosure, insurance policies mayinteract with one or more systems and/or methods described herein forenabling safe driving and lower rates for insurance policy customers. Inaddition, various approaches to helping users mitigate risk arepresented. In accordance with aspects of the disclosure, a computingdevice is disclosed for generating route traversal values in a datastore. The system may receive various types of information, includingbut not limited to, accident information, geographic information, andvehicle information, including autonomous driving information, from oneor more data sources and calculate a route traversal value forassociated road segments. Subsequently, the computing device may providethe associated route traversal value when provided with locationinformation for a road segment such as regional location informationand/or other information.

In an embodiment in accordance with aspects of the disclosure,route-dependent pricing uses route traversal values to adjust insurancepricing based on where a vehicle is driven. In this embodiment, aninsurance company (or its representatives, e.g., agent) may adjust theprice quoted/charged for an insurance policy based on risk consumed. Inthis embodiment, a vehicle/driver may be categorized into a risk class(e.g., low-risk, medium-risk, high risk, etc.) and charged for insuranceaccordingly. For example, the vehicle/driver may be provided withnotification of a credit/debit if the vehicle consumed less/more,respectively, of risk at the end of a policy term than was initiallypurchased.

In another embodiment: the insurance policy is sold and priced in partbased on where a customer falls within a three sigma distribution ofroute traversal values consumed by all insured per a typical policyperiod. The policy pricing may be based on an initial assumption of riskto be consumed in the prospective policy period or may be based on riskconsumed in a preceding policy period. In a case where the number ofroute traversal values consumed is greater than estimated, the customermay be billed for the overage at the end of (or during) the policyperiod. In yet another embodiment, the system may be provided as apay-as-you-drive coverage where the customer is charged in part based onthe actual route traversal values consumed in the billing cycle. Thesystem may include a telematics device that monitors, records, andperiodically transmits the consumption of route traversal values toprocessor 114 that may automatically bill or deduct the cost from anaccount.

Referring to FIG. 6, in another embodiment, an analysis of historicalaccident information can be performed to determine whether autonomous orsemi-autonomous or manual driving over a travel route provides less riskof accident. In an embodiment, a travel route for an autonomous orsemi-autonomous vehicle is received by the system (step 602). Ananalysis of historical accident information is performed for the travelroute. The analysis includes identifying accident information forvehicles engaged in autonomous or semi-autonomous driving over thetravel route and accident information for vehicles engaged in manualdriving over the travel route. An autonomous route traversal value forthe travel route is determined using historical accident information ofautonomous or semi-autonomous vehicles engaged in autonomous orsemi-autonomous driving over the travel route (step 604). A manual routetraversal value for the travel route is determine using historicalaccident information for vehicles engaged in manual driving over thetravel route (step 606). The autonomous route traversal value and themanual route traversal value is compared to determine whether autonomousor semi-autonomous driving or manual driving provides less risk ofaccident over the travel route (step 608). The determination for thetravel route can be stored in a database (step 610) for use in, forexample, future risk assessments of the travel route, making drivingdeterminations for an autonomous or semi-autonomous vehicle over thetravel route, and/or making manual driving decisions over the travelroute. For example, in an embodiment, the determination of whetherautonomous or semi-autonomous or manual driving provides less risk ofaccident over the travel route can be sent in a notification to thedriver/operator of the autonomous or semi-autonomous vehicle (step 612).

Referring to FIG. 7, in an embodiment, historical accident informationcan be used to adjust driving actions of an autonomous orsemi-autonomous vehicle over a travel route in order to avoid accidentswhich have occurred over the travel route. In an embodiment, a travelroute for an autonomous or semi-autonomous vehicle can be received oridentified (step 702). Historical accident information for the travelroute can be analyzed (step 704) to, for example, determine accidenttypes which occurred over the travel route. The analysis can identifyaccidents which occurred while driving manually or autonomously (step706) over the travel route. The analysis can include determining causesand/or probable causes of the accident types which occur over the travelroute (step 708). In response to determining accident types andcauses/probable causes of the accident types over the travel route,adjustments can be made to the driving actions planned for theautonomous or semi-autonomous vehicle over the travel route (step 710).The adjustments can be made based on the causes/probable causes of theaccident types in order to avoid the accident types during travel overthe travel route. For example, when a cause/probable cause of anaccident type over a travel route is determined to be excess speed, theadjustment of driving actins planned for the autonomous orsemi-autonomous vehicle can include a reduction of speed of travel ofthe autonomous or semi-autonomous vehicle over the travel route. Inaddition, for example, when a cause/probable cause of an accident typeover a travel route is determined to be lack of vehicle traction on theroad, the adjustment of driving actins planned for the autonomous orsemi-autonomous vehicle can include engagement of an all-wheel-drivefunction of the autonomous or semi-autonomous vehicle over the travelroute. In addition, for example, when a cause/probable cause of anaccident type over a travel route is determined to be a wildlifecrossing, the adjustment of driving actins planned for the autonomous orsemi-autonomous vehicle can include reduction of a speed of travel andpreparations for sudden braking and/or evasive maneuvers over the travelroute.

Referring to FIG. 8, in an embodiment, historical accident informationcan be used to analyze available travel routes and select a route whichpresents less risk of accident than others. In an embodiment, at leasttwo travel routes can be received by a risk analysis system (step 802).A route traversal value can be determined for each of the travel routes(step 804). The route traversal values for each travel route can becompared to determine which route provides less risk of accident overanother (step 806). A driver or autonomous or semi-autonomous drivingsystem can select a travel route on the basis that it provides less riskof accident than another travel route (step 808).

Some aspects of the present disclosure are directed toward methods,computer-readable media, software, systems and systems that provide avehicle-to-vehicle (V2V) communications system that may be used tocollect data from other vehicles. In certain embodiments, thevehicle-to-vehicle communications system involves automated analysis ofat least one moving vehicle by at least one other vehicle.

Vehicles in the driving analysis system may be, for example,automobiles, motorcycles, scooters, buses, recreational vehicles, boats,airplanes or other vehicles for which vehicle driving data may beanalyzed. The vehicles may or may not be insurance provider customers.The vehicles each include vehicle operation sensors capable of detectingand recording various conditions at the vehicle and operationalparameters of the vehicle. For example, sensors may detect and storedata corresponding to the vehicle's location (e.g., GPS coordinates),speed and direction, rates of acceleration or braking, and specificinstances of sudden acceleration, braking, and swerving. Sensors alsomay detect and store data received from the vehicle's internal systems,such as impact to the body of the vehicle, air bag deployment,headlights usage, brake light operation, door opening and closing, doorlocking and unlocking, cruise control usage, hazard lights usage,windshield wiper usage, horn usage, turn signal usage, seat belt usage,phone and radio usage within the vehicle, maintenance performed on thevehicle, and other data collected by the vehicle's computer systems.

Additional sensors may detect and store the external driving conditions,for example, external temperature, rain, snow, light levels, and sunposition for driver visibility. For example, external cameras andproximity sensors may detect other nearby vehicles, traffic levels, roadconditions, traffic obstructions, animals, cyclists, pedestrians, andother conditions that may factor into a driving event data analysis.Sensors also may detect and store data relating to moving violations andthe observance of traffic signals and signs by the vehicles. Additionalsensors may detect and store data relating to the maintenance of thevehicles, such as the engine status, oil level, engine coolanttemperature, odometer reading, the level of fuel in the fuel tank,engine revolutions per minute (RPMs), and/or tire pressure.

Vehicles sensors also may include cameras and/or proximity sensorscapable of recording additional conditions inside or outside of thevehicles. For example, internal cameras may detect conditions such asthe number of the passengers, types of passengers (e.g. adults,children, teenagers, pets, etc.) and identity of passengers in thevehicles, and potential sources of driver distraction within the vehicle(e.g., pets, phone usage, unsecured objects in the vehicle, etc.).

The data collected by vehicle sensors may be stored and/or analyzedwithin the respective vehicles, and/or may be transmitted to one or moreexternal devices. For example, sensor data may be transmitted viashort-range communication systems to other nearby vehicles.Additionally, the sensor data may be transmitted via telematics devicesto one or more remote computing devices.

Short-range communication systems are vehicle-based data transmissionsystems configured to transmit vehicle operational data to other nearbyvehicles, and to receive vehicle operational data from other nearbyvehicles. In some embodiments, communication systems may use thededicated short-range communications (DSRC) protocols and standards toperform wireless communications between vehicles. In the United States,75 MHz in the 5.850-5.925 GHz band have been allocated for DSRC systemsand applications, and various other DSRC allocations have been definedin other countries and jurisdictions. However, short-range communicationsystems need not use DSRC, and may be implemented using othershort-range wireless protocols in other embodiments, such as WLANcommunication protocols (e.g., IEEE 802.11), Bluetooth (e.g., IEEE802.15.1), or one or more of the Communication Access for Land Mobiles(CALM) wireless communication protocols and air interfaces. Thevehicle-to-vehicle (V2V) transmissions between the short-rangecommunication systems may be sent via DSRC, Bluetooth, satellite, GSMinfrared, IEEE 802.11, WiMAX, RFID, and/or any suitable wirelesscommunication media, standards, and protocols. In certain systems,short-range communication systems may include specialized hardwareinstalled in vehicles (e.g., transceivers, antennas, etc.), while inother examples the communication systems may be implemented usingexisting vehicle hardware components (e.g., radio and satelliteequipment, navigation computers) or may be implemented by softwarerunning on the mobile devices of drivers and passengers within thevehicles.

The range of V2V communications between vehicle communication systemsmay depend on the wireless communication standards and protocols used,the transmission/reception hardware (e.g., transceivers, power sources,antennas, etc.), and other factors. Short-range V2V communications mayrange from just a few feet to many miles, and different types of drivingbehaviors may be determined depending on the range of the V2Vcommunications. For example, V2V communications ranging only a few feetmay be sufficient for a driving analysis computing device in one vehicleto determine that another vehicle is tailgating or cut-off the vehicle,whereas longer communications may allow the device to determineadditional types of driving behaviors (e.g., yielding, defensiveavoidance, proper response to a safety hazard, etc.).

V2V communications also may include vehicle-to-infrastructure (V2I)communications, such as transmissions from vehicles to non-vehiclereceiving devices, for example, toll booths, rail road crossings, androad-side traffic monitoring devices. Certain V2V communication systemsmay periodically broadcast data from a vehicle to any other vehicle, orother infrastructure device capable of receiving the communication,within the range of the vehicle's transmission capabilities. Forexample, a vehicle 210 may periodically broadcast (e.g., every 0.1second, every 0.5 seconds, every second, every 5 seconds, every 10seconds, every 20 seconds, every 30 seconds, etc.) certain vehicleoperation data via its short-range communication system, regardless ofwhether or not any other vehicles or reception devices are in range. Inother examples, a vehicle communication system may first detect nearbyvehicles and receiving devices, and may initialize communication witheach by performing a handshaking transaction before beginning totransmit its vehicle operation data to the other vehicles and/ordevices.

The types of vehicle operational data, or vehicle driving data,transmitted by vehicles may depend on the protocols and standards usedfor the V2V communication, the range of communications, and otherfactors. In certain examples, vehicles may periodically broadcastcorresponding sets of similar vehicle driving data, such as the location(which may include an absolute location in GPS coordinates or othercoordinate systems, and/or a relative location with respect to anothervehicle or a fixed point), speed, and direction of travel. In certainexamples, the nodes in a V2V communication system (e.g., vehicles andother reception devices) may use internal clocks with synchronized timesignals, and may send transmission times within V2V communications, sothat the receiver may calculate its distance from the transmitting nodebased on the difference between the transmission time and the receptiontime. The state or usage of the vehicle's 210 controls and instrumentsmay also be transmitted, for example, whether the vehicle isaccelerating, braking, turning, and by how much, and/or which of thevehicle's instruments are currently activated by the driver (e.g., headlights, turn signals, hazard lights, cruise control, 4-wheel drive,traction control, etc.). Vehicle warnings such as detection by thevehicle's internal systems that the vehicle is skidding, that an impacthas occurred, or that the vehicle's airbags have been deployed, also maybe transmitted in V2V communications. In various other examples, anydata collected by any vehicle sensors potentially may be transmitted viaV2V communication to other nearby vehicles or infrastructure devicesreceiving V2V communications from communication systems. Further,additional vehicle driving data not from the vehicle's sensors (e.g.,vehicle make/model/year information, driver insurance information,driving route information, vehicle maintenance information, driverscores, etc.) may be collected from other data sources, such as adriver's or passenger's mobile device, driving analysis server, and/oranother external computer system, and transmitted using V2Vcommunications to nearby vehicles and other receiving devices usingcommunication systems.

The data collected by vehicle sensors also may be transmitted to adriving analysis server, and one or more additional external servers anddevices via telematics devices. Telematics devices may be computingdevices containing many or all of the hardware/software components asthe computing device. As discussed above, the telematics devices mayreceive vehicle operation data and driving data from vehicle sensors,and may transmit the data to one or more external computer systems(e.g., driving analysis server of an insurance provider, financialinstitution, or other entity) over a wireless transmission network.Telematics devices also may be configured to detect or determineadditional types of data relating to real-time driving and the conditionof the vehicles. In certain embodiments, the telematics devices maycontain or may be integral with one or more of the vehicle sensors. Thetelematics devices also may store the type of their respective vehicles,for example, the make, model, trim (or sub-model), year, and/or enginespecifications, as well as other information such as vehicle owner ordriver information, insurance information, and financing information forthe vehicles.

The telematics devices may receive vehicle driving data from vehiclesensors, and may transmit the data to a driving analysis server.However, in other embodiments, one or more of the vehicle sensors may beconfigured to transmit data directly to a driving analysis serverwithout using a telematics device. For instance, telematics devices maybe configured to receive and transmit data from certain vehicle sensors,while other sensors may be configured to directly transmit data to adriving analysis server without using the telematics devices. Thus,telematics devices may be optional in certain embodiments.

The system described in this disclosure for controlling an autonomous orsemi-autonomous vehicle may comprise a processor 114 connected to amemory 116, which contains a number of computer-executable instructionsand risk information for the desired driving route. The processor isconfigured to execute the computer-executable instructions and tocommunicate these instructions to the autonomous or semi-autonomousvehicle via a telematics device. The telematics device is able tocommunicate with both the processor and the navigation device 110 of theautonomous or semi-autonomous vehicle, in order to share desired routeinformation, including start and end locations, risk information,real-time route and vehicle information, and route alterations betweenthe processor 114 and navigation device 110 of the vehicle. A desiredroute may be composed of a system of road ways which, when followed,will lead from the start and end locations requested. In certainembodiments, there may be a plurality of routes which may lead from thestart location to the end location.

In addition to a vehicle navigation device, the autonomous orsemi-autonomous vehicle contains one or more sensors 130 on at least oneindividual vehicle component. Individual vehicle components may includeany area of the vehicle, including parts such as: windshield wipers,tires, brakes, suspension, headlights, brake lights, internal lights,battery, transmission, engine, or any other component of the vehicle orcombinations thereof. These sensors are able to communicate with theprocessor via the telematics device in order to provide real-time routeand vehicle information to the processor 114. The processor 114 maycontinuously recalculate the route traversal value for the driving route1006 based on the historical route traversal information and vehicle androute information communicated in real-time 1012 and communicate alteredroute or driving instructions to the vehicle navigation device 110 inorder to follow the route with the lowest route traversal value 1010.The sensors communicate this information with the processor as it isdetected, i.e. in real-time, as opposed to storing the information thatis collected to be communicated at a later time to the processor. Thehistorical route traversal information can consist of a variety offactors to that are taken into consideration by the processor 114 whendetermining the route with the lowest route traversal value for theroute, environment, and vehicle, including, but not limited to, accidentinformation 202, geographic information 204, vehicle information 206,route traversal value associated with a road segment 208, andcombinations thereof. The environment may refer to conditions such as:weather conditions, sunlight angle, time of day, temperature, windspeed, road hazards, topography, or other possible conditions externalto the vehicle which may affect the route traversal value of the route.

The one or more sensors 130 contained in autonomous or semi-autonomousvehicle may be capable of detecting previously unknown or unforeseenhazards on the selected route and communicate the detected hazard to theprocessor via the telematics device. For instance, the sensor may detectand gather information about an unknown hazard on the route and beunable to identify the hazard. In this case, the sensor may thencommunicate the information it was able to gather about the hazard tothe processor. Once the processor has received this hazard information,the processor analyzes the hazard information in order to determine thesize and type of hazard. The information is then stored in the memoryand is assigned a route traversal value. That route traversal value maythen be used to recalculate the route traversal value for the drivingroute and other possible routes in order to determine which has thelowest route traversal value. This calculation is done in real time inorder to allow the autonomous or semi-autonomous vehicle to react to thehazard before coming into contact with the hazard. Once thisdetermination is made, the processor may communicate to the vehiclenavigation device via the telematics device to stay on the currentdriving route or to alter the driving route in order to minimize theroute traversal value.

In one embodiment, the sensors 130 may include a camera, which maydetect a hole on the road on the route. The sensors 130 may then collectinformation about the hole that the camera detected and may communicatethat information to the processor via the telematics device. Theprocessor may then analyze that information using a variety ofalgorithms. In one instance, the processor may compare the autonomous orsemi-autonomous vehicle location on the selected route with riskinformation about that route previously stored in the memory in order todetermine the route traversal value for the hole. In another instance,the processor may analyze the information communicated by the sensor inorder to determine the size and severity of the hole and assign it aroute traversal value. In yet another instance, the processor maycommunicate with the vehicle navigation systems of other vehicles whichare traveling or have traveled the same route to determine the routetraversal value that they calculated for the hole. Once the hole has hada route traversal value assigned to it, the processor may calculate theroute traversal value for the current route, including the hole, andcompare it to the route traversal value of alternative routes whichavoid the hole. The processor then may select the route with the lowestroute traversal value, and may alter the current route to avoid the holeor continuing on the current route if the hole is not severe enough toraise the route risk score higher than that of other possible routes.

The sensors 130 may allow the system to identify problems withindividual vehicle components and a plurality of individual vehiclecomponents may be controlled by the system. In this embodiment, a sensoron an individual vehicle component may communicate the status of theindividual vehicle component with the processor via the telematicsdevice at regular intervals. The processor may analyze the individualvehicle component information against parameters set for each individualvehicle component stored in the memory. The information may also bestored in the memory in order to be used by the processor to determinethe typical status of an individual vehicle component or to helpanticipate an alteration that may need to be made in advance. Theprocessor may determine through the analysis comparing the parametersand real-time individual vehicle component information that it isnecessary to alter the status of the individual vehicle component inorder to minimize the route traversal value for the autonomous orsemi-autonomous vehicle. The system may then communicate the necessaryalteration to the vehicle navigation system via the telematics device.The vehicle navigation system may then communicate with the individualvehicle component in order to complete the alteration in order tominimize the route traversal value.

By identifying problems with individual vehicle components, it allowsthe system to alter the individual components in order to reduce theroute traversal value for the vehicle. Problems with vehicle componentsmay be the result of normal wear-and-tear, unexpected road hazards,previous accidents, or weather conditions. For example, in oneembodiment, sensors, such as a moisture detector, may detect that routeconditions are unusually wet, for instance if it is raining, andpreemptively squeegee the brake pads more often than if weatherconditions were dry in order to minimize route traversal value.

In another example, an autonomous or semi-autonomous vehicle may havepressure sensors on each of the tires in order to monitor air pressurein the tires. As the vehicle navigates the route, the pressure sensorsconstantly communicate the pressure in each of the tires with theprocessor via the telematics device. Each of those transmissions isanalyzed by the processor and stored in the memory in order to determineif the tire pressures are at the optimal level to minimize the routetraversal value. If a tire pressure is detected by the pressure sensors,communicated to the processor, and analyzed by the processor as beingoutside of the optimal tire pressure range, as determined by theparameters stored in the memory, the processor may determine thealteration needed in order to minimize the route traversal value of thevehicle. This alteration may then be communicated to the vehiclenavigation device, which may then communicate to the device in theautonomous or semi-autonomous vehicle which controls the pressure of thetires. The device which controls the pressure of the tires may thenalter the tire pressure until the pressure sensor communicates a readingto the processor which is analyzed to be within the normal parameters.

As shown in FIG. 9, the vehicle 100 may be outfitted with the personalnavigation device 110 already described above. In addition, one or moresecond vehicles 140 may be outfitted with personal navigation device(s)142, as also shown in FIG. 9. The processor 114 may communicate with theone or more personal navigation devices 142 of the one or more secondvehicles 140 via one or more telematics devices in order to receive, forinstance, real-time route and/or vehicle information collected by one ormore sensors 160 of the second vehicle(s). The communication between theprocessor 114 and each of the personal navigation devices 142 of thesecond vehicles 140 may be facilitated via an input 144 of therespective personal navigation device 142, accepting (e.g., wirelessly)information from the processor 114, and via an output 146 of therespective personal navigation device 142, sending (e.g., wirelessly)information to the processor 114. This feature may be used torecalculate route traversal values for the driving route using the routecondition information received from the second vehicle 140 in order tominimize route traversal values associated with the route conditioninformation.

In some embodiments, the second vehicle 140 outfitted with the personalnavigation device 142 in communication with the processor 114 may befollowing the same or a similar route as the vehicle 100. The secondvehicle 140 may, for example, detect a previously unknown road hazardvia one or more of the sensors 160, which may be, for instance, acamera, and infrared device, radar, and/or the like. The sensors maycommunicate the detected hazard to the processor via the telematicsdevice. Once the processor has received this hazard information, theprocessor analyzes the hazard information in order to determine the sizeand type of hazard. The information is then stored in the memory and isassigned a route traversal value. That route traversal value may then beused to recalculate the route traversal value for the driving route andother possible routes in order to determine which has the lowest routetraversal value. This calculation may be done in real time in order toallow the autonomous or semi-autonomous vehicle to react to the hazardbefore coming into contact with the hazard. After making thedetermination, the processor may communicate to the vehicle navigationdevice to stay on the current driving route or to alter the drivingroute in order to reduce the route traversal value.

The sensor information obtained by the second vehicle 140 may becommunicated to the computing device 102 in order to, for instance,collect information regarding hazards or risks recorded by variousvehicles and determine risk information for a route. For example, if thesecond vehicle 140 has encountered a hole (e.g., a pothole in a road),the second vehicle 140 may communicate relevant risk information (suchas the dimensions of the hole, the location of the hole, and/or a routetraversal value assigned to the hole) to the computing device 102. Thecomputing device 102 may combine the risk information gathered from thesecond vehicle 140 and/or other vehicles to determine risk informationfor the hole.

Further, the computing device 102 may aggregate risk information formultiple hazards along a route. The aggregated risk information maycomprise individual hazards or other risks associated with a route, andmay be used to determine risk information for the route itself. Forexample, if multiple holes have been reported along a route, thecomputing device 102 may determine that the route has a high degree ofrisk due to the severity of the holes. The computing device 102 may thenassign a route traversal value to the route involving the holes, basedon one or more properties of the holes such as the distribution of theholes (e.g., hole density), the locations of the holes, the sizes of theholes, and/or the quantity of the holes.

The risk information for a route may be used to assist in the operationof vehicles. A vehicle 100 (which may be autonomous or semi-autonomous)may be attempting to travel from a first destination to a seconddestination. The vehicle 100 may attempt to use a personal navigationdevice 110 to establish the route between the two destinations. As partof establishing the route, the personal navigation device 110 maycommunicate with the computing device 102 to determine risk informationfor one or more possible routes. The personal navigation device 110 maythen factor in the risk information into the route.

In one example, a car or other vehicle 100 may wish to travel from onepart of an urban area to another part of the urban area. One route maybe the fastest, but may have undergone recent construction causing poorroad conditions. The personal navigation device 110 of the vehicle 100may request information regarding the various routes from the computingdevice 102. Using information gathered by the sensors 130 and/or sensors160, such as braking information, information regarding the impact withthe road, the size of holes, etc., the computing device 102 maydetermine that the fastest route also has poor road conditions. Thecomputing device 102 may then suggest to the personal navigation device110 that an alternate, slightly longer route should be taken so as toavoid the poor road conditions. In an autonomous or semi-autonomousvehicle, this may have the advantage of reducing the wear and tear onthe vehicle and other risk factors while increasing the comfort levelfor the driver and/or passengers. If the autonomous vehicle chooses aroute independent of the driver, this may be done seamlessly so as tomaximize the experience for a vehicle's passengers with minimalintrusion.

In some instances, the risk information could be used to optimize travelalong a given route. By using the risk information gathered by one ormore vehicles, a future vehicle traveling down a known route may be ableto mitigate, and even avoid, certain risk factors. For example, thevehicle 100 may be traveling down a route with a known hole. Thenavigation systems of the vehicle 100 may be informed by the computingdevice 102 that the hole exists at a certain location. The vehicle 100may then deviate from a usual course to avoid the hole, or it may slowdown so as to minimize the risk of damage resulting from driving overthe hole. This may have the advantage of minimizing risk throughautomation and digital communications with a repository that may beimpossible to achieve for a vehicle dependent upon a human driver foroperation.

Autonomous or semi-autonomous vehicles may use collected information toaugment automated driving systems. Autonomous or semi-autonomousvehicles may utilize systems to detect and react to the environment. Forexample, a vehicle 100 may approach a street sign that, because of theangle of the sun, is undetectable by the vehicle sensors 130. In orderto determine what the sign says, the vehicle navigation device mayexamine the sign to determine, for example, text on the sign, the shapeof the sign, the color of the sign, and/or the location of the sign. Thevehicle 100 may then determine what type of sign it is based on theinformation and act accordingly. For example, the vehicle 100 mayidentify a sign as a stop sign, and stop at an intersection.

Shared information may be used to augment this process. When the vehicle100 stops at the stop sign, it may communicate the location of the stopsign to the computing device 102. A second vehicle 140 may approach thesign. The second vehicle 140 may attempt to read the sign, but may beunable to do so. For example, a setting sun in front of the vehicle mayblind the sensors 160, rendering the vehicle 140 incapable of readingthe sign. The vehicle 140 may know that a sign is present, but, forexample, may be unaware if it is a no parking sign, a yield sign, or astop sign. The vehicle 140 may then communicate with the computingdevice 102 to request information regarding the sign. The vehicle 140may supply information such as geographic location or sensor informationto help the computing device 102 identify the sign in question. Thecomputing device 102 may then determine that the sign is the stop signidentified by the vehicle 100, and communicate this information to thevehicle 140. The vehicle 140 may then stop in front of the stop sign.This may have the advantage of reducing the risks of autonomous orsemi-autonomous vehicle operation by crowd sourcing risk informationbetween multiple vehicles.

In some instances, information may be shared directly between vehicles.Vehicle to vehicle communication may be used to supply informationbetween vehicles to facilitate better autonomous or semi-autonomousvehicle operation. For example, a first autonomous car may pull up to asign. After the first autonomous car determines that the sign is a stopsign, it may communicate this information to the vehicles around thefirst autonomous car. If a second autonomous car pulls up to the signimmediately after the first autonomous car, it may use the informationsupplied by the first autonomous car to supplement a determination bythe second autonomous car that the sign is a stop sign.

A user in the autonomous or semi-autonomous vehicle may be able tooverride the system in order to begin driving the vehicle manually. Ifthis were to occur, the sensors may, in some embodiments, continue tocollect data and transmit the real-time vehicle and route information tothe process, including information regarding the decisions made by theuser manually controlling the vehicle. The processor may then use thatinformation to determine the route traversal value for the route thatthe user chose to drive 606, versus the route that the system may havechosen 604, and compare the two routes to determine which has the lowerroute traversal value 608. The processor may then store the informationfrom this choice in the memory to be used to calculate route traversalvalues for future driving routes for autonomous and semi-autonomouscars.

In some instances, the user may be able to control the level of riskassociated with a vehicle. A computing device 102 may determinedifferent route traversal values for different routes. Further, theremay be different route traversal values for different types of driving.For example, a route traversal value may be assigned to traveling at afirst speed for a route, and a second route traversal value may beassigned to traveling at a second speed for a route. The user may bepresented with the option of selecting which speed and associated routetraversal value they wish to use. For example, a user may be runninglate, and may accept a higher rate of risk.

Different route traversal values for a route may be controlled by orcontrol insurance information for a route. For example, when selecting aroute, a user may be presented with the route traversal value for thatroute. The user may also optionally select driving characteristics, suchas traveling faster or more aggressively. This may increase or decreasethe route traversal value. After selecting an option, the user may bepresented with an insurance cost for traveling on the route, where theinsurance cost is based at least in part on the route traversal valuefor the route and driving characteristics selected.

In some instances, a user may have a preconfigured setting foracceptable route traversal values for a route. For example, a user mayhave bought a discount insurance that only accepts route traversalvalues up to a certain level. When selecting a route, an autonomous orsemi-autonomous vehicle 100 may request route traversal valuesassociated with the route from the computing device 102. If the routetraversal values are higher than a threshold allowed under the user'sinsurance policy, the autonomous or semi-autonomous vehicle 100 mayattempt to find an alternative route that is below the threshold. Insome instances, a route may have multiple values associated withdifferent driving characteristics, and the autonomous or semi-autonomousvehicle 100 may select driving characteristics below the threshold. Forexample, a car may elect to travel on the interstate, but a thresholdvalue may require that the car only travel in the slow lane in order toreduce the chances of a collision.

In some embodiments, an autonomous or semi-autonomous vehicle 100 beingdriven in a high risk or difficult to maneuver area may be more safelyand efficiently navigated. For example, historical route traversalinformation and real-time route and vehicle information from a secondvehicle 140 for driving routes containing roundabouts and parkinggarages, which are historically difficult for autonomous vehicles tonavigate, may allow the vehicle navigation system to improve drivingresults. Data and information previously collected from other vehiclesand routes may be used by the system to continuously improve the drivingroutes and lower the route traversal value for these difficult tomaneuver areas.

FIG. 5 depicts an exemplary method of using and adjusting routetraversal values consistent with the disclosures provided herein. Thesteps depicted in the flow chart of FIG. 5 (as in all of the flow chartsdiscussed herein) are merely illustrative and may be combined, furthersubdivided, rearranged, and/or omitted as desired. Each of the depictedsteps may be performed by any one or more of the elements describedherein, such as one or more computing devices and/or systems. At step502, a system (such as a computing device 102) may receive startingand/or ending locations for the vehicle 100. For example, the personalnavigation device 110 may provide starting and/or ending locationsselected by a user to the system. At step 504, the system may determineinformation for possible driving routes. For example, the system maydetermine one or more reasonable routes between the locations, andhistorical route traversal information about the routes (i.e., accidentsrecorded on the routes, road condition information, traffic information,speed information, etc.).

At step 506, the system may determine one or more route traversal valuesfor a plurality of routes, such as for a set of possible (e.g.,feasible) routes. The system may determine one or more route traversalvalues for each route based on, for example, the distance of the route,obstacles along the route, accidents reported along the route, trafficalong the route, the length of the route, the speed limit of the routeand/or other information. The route traversal values may be specific to(e.g., associated with) certain categories (e.g., time to arrival,distance traveled, risk of an accident, wear on the vehicle, etc.),and/or the risk traversal values may be values computed by weightinginformation relating to the certain categories. The weights may bedependent on one or more characteristics specified by and/or for thedriver and/or another user. For example, a driver may specify that ashorter route is more important than a lower risk. In another example,an insurance policy for the driver may require the driver to take lowerrisks, and the risk of an accident may be weighted more highly. At step508, the system may determine the route with the lowest route traversalvalue. For example, the system may compare the route traversal valuesfor each route and determine the route with the lowest route traversalvalue. Routes with certain areas or route traversal values above athreshold may be ignored. For example, if the risk of accident is toogreat, the system may not consider a route even though it has the lowestoverall route traversal value.

At step 510, the vehicle 100 may begin traveling down the route with thelowest route traversal value. For example, after receiving an indicationof a route with the lowest route traversal value from the system, avehicle 100 may indicate the route on a personal navigation device 110and/or automatically begin driving along (or otherwise traversing) theroute. As the vehicle travels along the route, the vehicle 100 maycapture route and vehicle information via sensors 130. For example, thesensors 130 may detect signage, traffic, bumps in a road, the speed ofthe vehicle, and/or other such information. At step 512, the route andvehicle information may be transmitted to the system. Route and vehicleinformation may also be received by the system from a second vehicle 140in step 514. For example, the second vehicle 140 may relay informationto the vehicle 100 via vehicle-to-vehicle communication and/or relayinformation to the system.

At step 516, the system may adjust the route traversal value for theroute. The route traversal value may be adjusted based on newinformation from vehicles traveling along the route. For example, thesystem may determine that route has a significant amount ofconstruction, and raise the route traversal value based on informationreceived from the vehicle 100 and a second vehicle 140. The secondvehicle 140 may be traveling ahead of the vehicle 100 along the route.If the route traversal value of the route changes, the method mayrepeat, using the current location of the vehicle 100 as the startlocation and the preexisting ending location as the end location. Thesystem may then determine a new route by repeating the method asdescribed above.

While the disclosure has been described with respect to specificexamples including presently exemplary modes of carrying out thedisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above-described systems andtechniques that fall within the spirit and scope of the disclosure.

I claim:
 1. A system for controlling a vehicle comprising at least oneof an autonomous vehicle and a semi-autonomous vehicle, the systemcomprising: a processor configured to execute computer-executableinstructions; a telematics device communicatively coupled to thevehicle, the processor, at least one sensor, and a vehicle navigationdevice; and a memory storing the computer-executable instructions that,when executed by the processor, cause the system to perform stepscomprising: receiving location information from a plurality of vehiclenavigation devices each corresponding to one of a plurality of vehicles;receiving route hazard information from a plurality of telematicsdevices each corresponding to one of the plurality of vehicles;calculating a plurality of route traversal values, respectively, basedat least on associating the location information with the route hazardinformation; receiving, from the vehicle navigation device, a requestfor a route to a destination; selecting, based on one or more of theplurality of route traversal values, the route; and transmitting, to thevehicle, route information corresponding to the route.
 2. The system ofclaim 1, wherein the computer-executable instructions, when executed bythe processor, cause the system to adjust the one or more of theplurality of route traversal values based on receiving an indication ofuser input.
 3. The system of claim 1, wherein the calculating theplurality of route traversal values comprises utilizing previouslystored route traversal value information, previously stored vehicleinformation, previously stored route information, and historical routetraversal information to calculate the route traversal values.
 4. Thesystem of claim 1, wherein the vehicle navigation device comprises aglobal positioning satellite unit configured to determine a location ofthe vehicle.
 5. The system of claim 1, wherein the computer-executableinstructions, when executed by the processor, cause the system to adjustat least one individual vehicle component based at least in part uponthe route information.
 6. The system of claim 1, wherein thecomputer-executable instructions, when executed by the processor, causethe system to adjust at least one individual vehicle component basedupon a weather condition detected by the at least one sensor.
 7. Thesystem of claim 1, wherein the route information comprises directionalinformation and route hazard information.
 8. The system of claim 1,wherein the computer-executable instructions, when executed by theprocessor, cause the system to adjust the route based on a route hazarddetected by the at least one sensor, and wherein the at least one sensoris associated with the vehicle.
 9. The system of claim 1, wherein the atleast one sensor comprises at least one camera.
 10. The system of claim1, wherein the calculating the plurality of route traversal valuescomprises calculating the plurality of route traversal values based onaccident information associated with a road segment, geographicinformation associated with the road segment, vehicle informationassociated with the road segment, and a traversal value associated withthe road segment.
 11. The system of claim 1, wherein the selecting,based on the one or more of the plurality of route traversal values, theroute comprises selecting the route by determining the route associatedwith a lowest route traversal values of the plurality of route traversalvalues.
 12. The system of claim 1, wherein the computer-executableinstructions, when executed by the processor, cause the telematicsdevice to transmit accident information for the vehicle.
 13. The systemof claim 1, wherein the calculating the plurality of route traversalvalues further comprises calculating the plurality of route traversalvalues based on accident information.
 14. A method for controlling avehicle comprising one of an autonomous and a semi-autonomous vehicle,comprising: receiving a start location and an end location from avehicle navigation device; determining historical route traversalinformation for a plurality of possible routes corresponding to thestart location and the end location, wherein the historical routetraversal information is based on received route traversal informationfrom a plurality of vehicles regarding the plurality of possible routes;calculating a plurality of route traversal values based on thehistorical route traversal information; selecting, based on theplurality of route traversal values, a route of the plurality ofpossible routes corresponding to a lowest route traversal value usingthe historical route traversal information; receiving vehicleinformation and route condition information for the route from atelematics device based on data obtained from at least one individualvehicle component sensor of the vehicle; recalculating the routetraversal value for each available route based on the vehicleinformation and the route condition information; and in response to therecalculating the route traversal value, transmitting, to the vehicle,an altered route to reduce the route traversal value.
 15. The method ofclaim 14, wherein the received route traversal information is received,at least in part, via vehicle to vehicle communication.
 16. The methodof claim 15, wherein the calculating the route traversal valuescomprises utilizing previously stored route traversal value information,previously stored vehicle information, previously stored routeinformation, and historical route traversal information to calculate theroute traversal values.
 17. An apparatus for a vehicle comprising one ofan autonomous and a semi-autonomous vehicle, comprising: a processor; apersonal navigation device associated with the vehicle; a telematicsdevice; and a memory storing computer-executable instructions, that whenexecuted by the processor, cause the apparatus to: receive, from thepersonal navigation device, a start and end location for a desired routefrom the personal navigation device; calculate a first route traversalvalue for a first route and a second route traversal value for a secondroute based on historical route traversal information for the firstroute, historical route traversal information for the second route,historical route traversal information for an environment, andhistorical route traversal information for a plurality of vehicles;determine a selected route by comparing whether the first routetraversal value is lower than the second route traversal value; transmitthe selected route to the vehicle; receive, from a plurality of sensorsof the vehicle, vehicle condition information and route conditioninformation for the selected route, wherein the selected route is beingtraveled by the vehicle; recalculate the first route traversal value forthe selected route and an alternative route traversal value for a thirdroute based on historical route traversal information for the selectedroute, historical route traversal information for the third route,historical route traversal information for the environment, historicalroute traversal information for the vehicle, the vehicle conditioninformation, and the route condition information; receive, from a secondvehicle, a start and end location for a desired route; determine asecond selected route by comparing whether the first route traversalvalue is lower than the alternative route traversal value; and transmitthe second selected route to the second vehicle.
 18. The apparatus ofclaim 17, the apparatus further comprising computer-executableinstructions, which, when executed by the processor, cause the apparatusto communicate the vehicle condition information and the route conditioninformation to the second vehicle.
 19. The apparatus of claim 17, theapparatus further comprising computer-executable instructions, which,when executed by the processor, cause the apparatus to recalculate thefirst route traversal value based on second vehicle conditioninformation and second route condition information received from thesecond vehicle.
 20. The apparatus of claim 17, wherein transmitting thesecond selected route comprises sending the second selected route viavehicle to vehicle communication.